相关申请交叉援引Related Application Cross Reference
本申请要求2016年1月12日提交的第62/277,786号美国临时申请的优先权，该临时申请通过援引纳入本文如同在此全文呈现。This application claims priority to U.S. Provisional Application No. 62/277,786, filed January 12, 2016, which is incorporated herein by reference as if fully set forth herein.
关于联邦政府资助研发的声明Statement on Federal Funding for Research and Development
本发明藉美国国立卫生研究院授予的U01CA117374获得政府支持。政府对本发明享有一定权益。This invention was made with Government support under U01CA117374 awarded by the National Institutes of Health. The government has certain rights in this invention.
技术领域technical field
本文涉及肺癌领域的生物标志物复合物及检测。This article deals with biomarker complexes and assays in the field of lung cancer.
背景技术Background technique
肺癌死亡率下降与CT筛查相关，使得CT筛查用的越来越多，同时提高了良性肺结节的检出。这些个体中许多接受了不必要的昂贵的侵入性诊疗。所以需要一种伴随诊断来对肺结节个体进行高风险或低风险分级。The decline in lung cancer mortality is associated with CT screening, which has led to the increasing use of CT screening and improved detection of benign pulmonary nodules. Many of these individuals underwent unnecessarily expensive and invasive procedures. Therefore, a companion diagnostic is needed to stratify individuals with pulmonary nodules as high or low risk.
肺癌长期位居美国癌症死因前列，2014年死亡超过150,000例。肺癌的五年存活率总体仅17％，57％的肺癌确诊已是晚期，这些的五年存活率仅4％。目前，用低剂量计算机断层扫描(CT)对55至74岁具有广泛吸烟史的人群进行筛查。CT扫描被证明有效地将肺癌死亡率降低了20％，但是数百万CT检出的肺结节仍无法确诊是恶性还是良性。Lung cancer has long been the leading cause of cancer death in the United States, accounting for more than 150,000 deaths in 2014. The overall five-year survival rate of lung cancer is only 17%, and 57% of lung cancers are diagnosed at advanced stage, and the five-year survival rate of these is only 4%. Currently, people aged 55 to 74 with an extensive smoking history are screened with low-dose computed tomography (CT). CT scans have been proven effective in reducing lung cancer mortality by 20%, but millions of CT-detected lung nodules remain undiagnosed as malignant or benign.
据全国肺部筛查临床试验(NLST)，CT检出的结节中仅3.6％被确认为肺癌，可见假阳性率颇高。因此需要区分恶性结节与良性结节的诊断试验，改善与CT筛查的联合诊断。According to the National Lung Screening Trial (NLST), only 3.6% of the nodules detected by CT were confirmed as lung cancer, which shows that the false positive rate is quite high. Therefore, there is a need for diagnostic tests to distinguish malignant from benign nodules and to improve combined diagnosis with CT screening.
发明内容Contents of the invention
我们是对癌症患者、良性结节患者和健康吸烟者体内体液免疫应答最早的研究之一。我们最先在40名早期肺癌患者和40名吸烟者对照中采用核酸可编程蛋白阵列(NAPPA)鉴定候选癌症特异性自身抗体(AAb)，完成了对10,000种全长人蛋白质的血清反应性特征鉴定。在137名肺癌患者和127名吸烟者对照以及170良性肺结节主体中对有希望的候选物进行了ELISA试验。从蛋白质微阵列筛查实验—该实验采用40名患者和40名吸烟者对照的发现集—选出17个肺癌病例中反应性高于对照的抗原用于在大样品集中(n＝264)用酶联免疫吸附试验(ELISA)进行测评。We were one of the first studies of the humoral immune response in cancer patients, patients with benign nodules, and healthy smokers. We first identified candidate cancer-specific autoantibodies (AAbs) using nucleic acid programmable protein arrays (NAPPA) in 40 early-stage lung cancer patients and 40 smoker controls, completing the characterization of serum reactivity to 10,000 full-length human proteins Identification. Promising candidates were tested in ELISA in 137 lung cancer patients and 127 smoker controls, as well as in 170 subjects with benign pulmonary nodules. From a protein microarray screening experiment—which used a discovery set of 40 patients and 40 smoker controls—antigens with higher reactivity than controls in 17 lung cancer cases were selected for use in a large sample set (n=264) enzyme-linked immunosorbent assay (ELISA) for evaluation.
得出一个此前未知的5-AAb分类器(TTC14、BRAF、ACTL6B、MORC2、CTAG1B)，能够区分肺癌与吸烟者对照，89％特异性灵敏度为30％。我们还检测了CT阳性良性结节主体(n＝170)的AAb应答，得出一个5-AAb组(KRT8、TTC14、KLF8、BRAF、TLK1)，88％特异性灵敏度为30％。值得注意的是，基于TCGA数据集，6个AAb靶标(TTC14、BRAF、MORC2、CTAG1B、KRT8、TLK1)的mRNA水平在肺腺癌组织中也升高。因此，我们发现了此前未知的与肺腺癌相关的抗体/抗原复合物即Aab，潜在地，它们区分癌症与CT阳性的良性疾病。A previously unknown 5-AAb classifier (TTC14, BRAF, ACTL6B, MORC2, CTAG1B) was derived that was able to distinguish lung cancer from smoker controls with 89% specificity and 30% sensitivity. We also examined AAb responses in subjects with CT-positive benign nodules (n=170), resulting in a 5-AAb panel (KRT8, TTC14, KLF8, BRAF, TLK1) with 88% specificity and 30% sensitivity. Notably, based on the TCGA dataset, the mRNA levels of six AAb targets (TTC14, BRAF, MORC2, CTAG1B, KRT8, TLK1) were also elevated in lung adenocarcinoma tissues. We thus discovered a previously unknown antibody/antigen complex, Aab, associated with lung adenocarcinoma, potentially discriminating cancer from CT-positive benign disease.
附图说明Description of drawings
图1：研究方案设计。Figure 1: Study protocol design.
图2：从蛋白阵列筛查发现的肺癌相关抗原汇总，其中(A)显示蛋白阵列筛查选出的肺癌相关AAb靶标的GO富集分析。胚胎形态发生项有12个基因，其余节点各有3至4个基因。(B)显示肺癌与吸烟者对照相比的差异AAb应答热图。Figure 2: Summary of lung cancer-associated antigens discovered from protein array screening, where (A) shows GO enrichment analysis of lung cancer-associated AAb targets selected from protein array screening. The embryonic morphogenesis item has 12 genes, and the remaining nodes each have 3 to 4 genes. (B) Heat map showing differential AAb responses in lung cancer compared to smoker controls.
图3：组II各AAb的应答。各自的截止值即良性对照第98百分位值以实线标明。Figure 3: Responses of Group II AAbs. The respective cut-off values, the 98th percentile values of the benign controls, are indicated by solid lines.
图4：临床因素与AAb应答的多变量分析。A：肺癌和良性对照中吸烟史、AAb应答和结节大小分析。B：肺癌病例中吸烟史、疾病分期和AAb应答分析。(A和B中，吸烟情况的指标是纵坐标所示吸烟指数(pack-year)。结节大小以圆直径代表。)Figure 4: Multivariate analysis of clinical factors and AAb response. A: Analysis of smoking history, AAb response and nodule size in lung cancer and benign controls. B: Analysis of smoking history, disease stage and AAb response in lung cancer cases. (In A and B, the indicator of smoking status is the smoking index (pack-year) shown on the ordinate. The size of the nodule is represented by the diameter of the circle.)
图5：两组中AAb靶标的mRNA表达水平(TCGA)。仅对肺腺癌(ADC)和实体组织正常者(正常)的mRNA水平作图。Figure 5: mRNA expression levels of AAb targets in both groups (TCGA). The mRNA levels were plotted only for lung adenocarcinoma (ADC) and those with normal solid tissue (Normal).
具体实施方式Detailed ways
肺癌可触发宿主免疫应答并诱导抗肿瘤抗原的抗体。本文所述自身抗体(AAb)及其对应抗原的鉴定影响到我们对癌症免疫的认识，可实现早期诊断，甚至对免疫治疗亦有裨益。Lung cancer can trigger a host immune response and induce antibodies against tumor antigens. The identification of autoantibodies (AAbs) and their corresponding antigens described here affects our understanding of cancer immunity, enabling early diagnosis and even benefitting immunotherapy.
此前的研究主要基于癌症患者与健康(吸烟者)对照的比较。尤其，此类标志物的检测应当依赖于容易获得的样品，例如血浆或痰涎，因为此类检测可能对接受筛查的个体进行。为此，已经在蛋白质、循环肿瘤细胞、循环肿瘤DNA和循环miRNA的鉴定上进行了大量的努力。Previous research has largely been based on comparisons of cancer patients with healthy (smokers) controls. In particular, detection of such markers should rely on readily available samples, such as plasma or sputum, since such detection may be performed on individuals undergoing screening. To this end, considerable efforts have been devoted to the identification of proteins, circulating tumor cells, circulating tumor DNA, and circulating miRNAs.
血液中许多分子标志物的浓度会非常低，因为它依赖于癌细胞分泌，而癌细胞在临床前数量很低。通常，分泌生物标志物中仅少部分进入血浆，在血浆中，生物标志物被大量的血液所稀释。这样的低浓度造成发现和日常检测困难。The concentration of many molecular markers in the blood will be very low because it is dependent on secretion by cancer cells, which are low in preclinical numbers. Typically, only a small fraction of the secreted biomarkers enters the plasma, where they are diluted by the larger volume of blood. Such low concentrations make discovery and routine testing difficult.
另一种策略是利用免疫系统的能力通过产生自身抗体(AAb)来检测肿瘤细胞。抗靶标肿瘤抗原的这些过继免疫系统应答有效放大来自癌组织所释放微量肿瘤蛋白质的信号。Another strategy exploits the ability of the immune system to detect tumor cells by producing autoantibodies (AAbs). These adoptive immune system responses against target tumor antigens effectively amplify signals from tiny amounts of tumor proteins released by cancerous tissue.
我们在此描述最早之一的一项蛋白质组学研究，重点比较肺腺癌(ADC)患者、重度吸烟者(SMC)和良性结节对照(BNC)同质样本集中的血浆AAb应答。我们聚焦一个肺癌亚型来避免异质性并使得以排除亚型间差异。我们从非小细胞肺癌(NSCLC)腺癌亚型患者与年龄、性别、吸烟情况配对对照的无偏癌特异性抗体筛查开始，筛查采用展示约10,000种全长人蛋白质的核酸可编程蛋白阵列(NAPPA)进行。然后在独立的一组病例和对照中对候选肺癌特异性抗体进行评价，组中包括良性肺结节主体。We describe here one of the first proteomic studies focused on comparing plasma AAb responses in a homogeneous sample set of lung adenocarcinoma (ADC) patients, heavy smokers (SMC), and benign nodular controls (BNC). We focused on a lung cancer subtype to avoid heterogeneity and to exclude differences between subtypes. We started with an unbiased cancer-specific antibody screen of patients with non-small cell lung cancer (NSCLC) adenocarcinoma subtypes paired with age, sex, and smoking status using nucleic acid-programmable proteins displaying approximately 10,000 full-length human proteins array (NAPPA). Candidate lung cancer-specific antibodies were then evaluated in an independent cohort of cases and controls that included subjects with benign lung nodules.
我们鉴得的作为肺癌早期检测潜在生物标志物的19个抗原是：TL14、VPS72、CTTNBP2NL、TSPYL2、ACTL6B、ACVR2B、BRAF、KLF8、BAT4、C12ORF50、IQCE、CSPP1、KRT8、MORC2、FAM76A、NF2、TLK1、P53(TP53)、NYESO1(CTAG1B)。我们鉴定了一个AAb组来区分肺癌患者和配对吸烟者对照，89％特异性灵敏度为30％。此外，我们还鉴定了一个AAb组来区分肺癌患者与配对低剂量计算机断层扫描阳性良性对照，88％特异性灵敏度为30％。我们将样品指认为阳性，如果它们超过了组中任一AAb的抗原特异性截止值。Aab特异性截止值定为AAb各自98％特异性的水平。The 19 antigens we identified as potential biomarkers for early detection of lung cancer are: TL14, VPS72, CTTNBP2NL, TSPYL2, ACTL6B, ACVR2B, BRAF, KLF8, BAT4, C12ORF50, IQCE, CSPP1, KRT8, MORC2, FAM76A, NF2, TLK1, P53 (TP53), NYESO1 (CTAG1B). We identified an AAb panel to distinguish lung cancer patients from paired smoker controls with 89% specificity and 30% sensitivity. Furthermore, we also identified an AAb panel to distinguish lung cancer patients from paired low-dose computed tomography positive benign controls with 88% specificity and 30% sensitivity. We designated samples as positive if they exceeded the antigen-specific cutoff for any AAb in the panel. Aab specificity cutoffs were set at the level of 98% specificity for each AAb.
表1.所选AAb的发现和验证统计学数据Table 1. Discovery and validation statistics for selected AAbs
表2.组I各AAb的灵敏度和特异性Table 2. Sensitivity and specificity of each AAb of Group I
表3.组II各AAb的灵敏度和特异性.Table 3. Sensitivity and specificity of each AAb of Group II.
采用免疫-蛋白组学方法，我们鉴定了健康人、重度吸烟者对照和肺癌患者中抗体应答的特征。生物信息分析发现显著富集的胚胎形态发生、器官发育(包括肺发育)和受体信号传导相关通路以及丝氨酸/苏氨酸激酶通路。采用包括良性结节主体的扩展样品集用ELISA确认了抗体子集的性能，98％特异性灵敏度为5-10％。发现一个5-AAb组(TTC14、BRAF、ACTL6B、MORC2、CTAG1B)，其区分肺癌与有吸烟史高风险对照的89％特异性灵敏度为30％。Using an immuno-proteomics approach, we characterized antibody responses in healthy individuals, heavy smoker controls, and lung cancer patients. Bioinformatic analysis revealed significantly enriched pathways related to embryonic morphogenesis, organ development (including lung development), and receptor signaling, as well as the serine/threonine kinase pathway. The performance of the subset of antibodies was confirmed by ELISA with an extended sample set including the majority of benign nodules, with 98% specificity and sensitivity of 5-10%. A 5-AAb panel (TTC14, BRAF, ACTL6B, MORC2, CTAG1B) was found with a specificity of 89% and a sensitivity of 30% for distinguishing lung cancer from high-risk controls with a history of smoking.
肺癌与CT阳性肺结节患者的AAb应答比较发现一个相关但不同的5-AAb组(TTC14、BRAF、KLF8、TLK1、KRT8)，88％特异性灵敏度为30％。我们还计算了进入验证试验的全部19个候选抗原的灵敏度和特异性，所述验证试验要求良性样本的吸烟史吸烟指数达30或30以上。报道的全部五个候选抗原的98％特异性灵敏度仍然都高于5％，表明他们仍然能够区分肺腺癌与良性对照(不显示)。A comparison of AAb responses in patients with lung cancer and CT-positive pulmonary nodules identified a related but distinct group of 5-AAbs (TTC14, BRAF, KLF8, TLK1, KRT8) with 88% specificity and 30% sensitivity. We also calculated the sensitivities and specificities of all 19 candidate antigens entered into a validation assay requiring a smoking history index of 30 or more for benign samples. The 98% specificity sensitivities reported for all five candidate antigens were still above 5%, indicating that they were still able to distinguish lung adenocarcinoma from benign controls (not shown).
尽管这些组需进一步验证，它们确实提供了这些具有信息意义的抗原的互补性(complementarities)信息。进一步分析显示，AAb与肿瘤大小、分期和吸烟史没有关联。就我们所知，这是首次采用免疫-蛋白组学方法鉴定可能有助于将CT阳性结节主体分级为良性肺病对照和肺腺癌患者的研究。为了确保分析ELISA结果时应答测算准确，我们还测算了每份血浆样品的支持试剂相关背景，因此是类似研究中最严格的试验。Although these panels require further validation, they do provide information on the complementarities of these informative antigens. Further analysis revealed no association of AAbs with tumor size, stage, and smoking history. To our knowledge, this is the first study using an immuno-proteomic approach to identify subjects that may be useful in stratifying CT-positive nodule subjects into benign lung disease controls and lung adenocarcinoma patients. To ensure accurate response measurements when analyzing ELISA results, we also measured the background relative to supporting reagents for each plasma sample, and thus were the most rigorous of any study of its kind.
鉴于以上所述，创新之一在于鉴定了19个抗原作为生物标志物用于肺癌的早期检测。其中许多从未与肺癌关联。此外，我们还研发出了标志物组，它们以89％特异性和30％灵敏度区分肺癌患者与配对吸烟者对照，或者，以88％特异性30％灵敏度区分肺癌患者与CT筛查阳性的良性配对对照。In view of the above, one of the innovations is the identification of 19 antigens as biomarkers for early detection of lung cancer. Many of these have never been linked to lung cancer. In addition, we developed a panel of markers that distinguished lung cancer patients from matched smoker controls at 89% specificity and 30% sensitivity, or, lung cancer patients from benign patients with positive CT screening at 88% specificity and 30% sensitivity. Paired controls.
目前，没有临床可用的血检能够区分肺癌患者与计算机断层扫描阳性人群。并且，本文所述Aab复合物和抗原就肺癌检测和患者区分而言是新的。Currently, there are no clinically available blood tests that can distinguish lung cancer patients from those with positive computed tomography scans. Also, the Aab complexes and antigens described herein are novel for lung cancer detection and patient differentiation.
非限定性实施例non-limiting example
血浆样品描述Plasma sample description
共计434份来自NYU的血浆样品，其中，137份肺腺癌，127份有吸烟史的对照和170份良性肺结节(肉芽肿瘤，n＝47；肺气肿，n＝50；稳定结节，n＝73)。全部肺癌样品均于手术室手术期间采集并经病理确认。都未曾接受过治疗。获取后4小时内进行EDTA血浆样品处理并于-80℃冷冻保存。A total of 434 plasma samples were obtained from NYU, of which, 137 lung adenocarcinomas, 127 smoking history controls and 170 benign lung nodules (granuloma, n=47; emphysema, n=50; stable nodules , n=73). All lung cancer samples were collected during surgery in the operating room and confirmed by pathology. Neither received treatment. EDTA plasma samples were processed within 4 hours of acquisition and stored frozen at -80°C.
腺癌患者是在NYU癌症中心临床中招募的，均被告知并同意IRB批准的肺癌生物标志物中心肺癌计划#8896(“Lung Cancer Biomarker Center Lung Cancer Protocol#8896”)。我们的对照样本包括入组NYU肺癌生物标志物中心的高风险吸烟者。该队列的平均吸烟史的吸烟指数为42。我们还就吸烟情况与肺癌主体进行了配对，肺癌患者有些从不吸烟。所有对照样本的招募均得到IRB批准。这是一次自愿招募，由罗姆博士(Dr.Rom)及其研究护士通过信件、电话、访问工会初级保健医生和Con Edison。肺癌患者被转到哈维帕斯博士(Dr.Harvey Pass)处接受测评。NYU肺癌生物标志物中心进行低剂量CT-扫描筛查高风险吸烟者，此为国家癌症研究所的早期检测研究计划(National Cancer Institute's EarlyDetection Research Program)的一部分。Adenocarcinoma patients were recruited in the NYU Cancer Center Clinic, informed and consented to the IRB-approved Lung Cancer Biomarker Center Lung Cancer Protocol #8896 ("Lung Cancer Biomarker Center Lung Cancer Protocol #8896"). Our control sample included high-risk smokers enrolled in the NYU Lung Cancer Biomarker Center. The mean smoking index of the cohort's smoking history was 42. We also matched smoking status with lung cancer subjects, some of whom were never smokers. Recruitment of all control samples was approved by the IRB. This was a voluntary recruitment by letters, phone calls, interviews with union primary care physicians and Con Edison by Dr. Rom and his research nurse. Lung cancer patients were referred to Dr. Harvey Pass for evaluation. The NYU Lung Cancer Biomarker Center conducts low-dose CT-scans to screen high-risk smokers as part of the National Cancer Institute's Early Detection Research Program.
良性结节随访两年无发展。研究对象均无癌症和化疗史。全部对象均按照EDRN规程抽血、接受呼吸量测定、并收集吸烟史问卷调查。按照IASLC协议确定肺癌分期。The benign nodules were followed up for two years without development. None of the subjects had a history of cancer and chemotherapy. All subjects were drawn blood according to the EDRN protocol, received spirometry, and collected smoking history questionnaires. Lung cancer staging was determined according to the IASLC protocol.
在用于蛋白阵列实验的发现样本集中，40名肺腺癌患者根据年龄、性别和吸烟史与40名无癌症对照配对。来自发现样本集的40名患者中38位的疾病分期为I期。为了验证的目的，增加了97名不同疾病分期的肺腺癌患者(47％为I期)和87名对照以及170名CT阳性良性肺病患者。In the discovery sample set used for protein array experiments, 40 lung adenocarcinoma patients were paired with 40 cancer-free controls based on age, sex, and smoking history. The disease stage was stage I in 38 of 40 patients from the discovery sample set. For validation purposes, 97 patients with lung adenocarcinoma of different disease stages (47% stage I) and 87 controls and 170 patients with CT-positive benign lung disease were added.
蛋白阵列实验protein array experiment
开放阅读框从DNASU(https://dnasu.org/)获得。蛋白阵列的生产和质控实验按照先前的记载进行。简言之，制造展示10,000种人蛋白质的阵列(均匀分配于5个阵列集)。血浆检测实验采用HS 4800TM专业杂交工作站(“Pro hybridization station”)(帝肯(Tecan))进行。简言之，玻片先用SuperBlock(皮尔斯(Pierce)孵育，然后用“一步人偶联体外表达系统”(“1-Step Human Coupled in vitro Expression system”)赛默(Thermo))表达蛋白质。用含Tween 20的含乳5％磷酸盐缓冲液(含乳PBST)封闭后，玻片与血浆样品(1:50，大肠杆菌裂解物制备的5％含乳系预孵2-3小时)于4℃孵育16小时，接着用5％含乳PBST冲液3遍。然后，玻片与Dylight649标记的山羊抗人IgG(杰克森免疫研究实验室“JacksonImmunoResearch Laboratories”)于23℃孵育1小时。然后对玻片进行清洗、干燥，帝肯(Tecan)扫描仪扫描，扫描设置相同。Open reading frames were obtained from DNASU (https://dnasu.org/ ). The production and quality control experiments of protein arrays were performed as previously described. Briefly, arrays displaying 10,000 human proteins (evenly distributed across 5 array sets) were fabricated. Plasma detection experiments were performed using HS 4800TM professional hybridization workstation ("Pro hybridization station") (Tecan). Briefly, slides were incubated with SuperBlock (Pierce) followed by protein expression using the "1-Step Human Coupled in vitro Expression system" (Thermo). After blocking with Tween 20-containing 5% milk-containing phosphate buffer solution (milk-containing PBST), slides and plasma samples (1:50, 5% milk-containing line prepared from E. coli lysates were pre-incubated for 2-3 hours) in Incubate at 4°C for 16 hours, then rinse with 5% milk-containing PBST three times. Slides were then incubated with Dylight649-labeled goat anti-human IgG (Jackson ImmunoResearch Laboratories "Jackson ImmunoResearch Laboratories") for 1 hour at 23°C. The slides were then washed, dried, and scanned with a Tecan scanner with the same scanning settings.
蛋白阵列图像分析和定量Protein array image analysis and quantification
蛋白阵列扫描图用“阵列专业分析仪(“ArrayPro Analyzer”)”(MediaCybernetics公司)来分析。为了捕捉无法由图像分析软件定量的真性抗体应答，两名研究人员对全部图像进行定性检查来鉴定和确认阳性应答，过程如此前所述。简言之，用“ArrayPro Analyzer”(MediaCybernetics公司)将原始图像调到极高对比度和亮度，据环的强度和形态将每个斑点分为0至5级。Protein array scans were analyzed using "ArrayPro Analyzer" (MediaCybernetics, Inc.). To capture authentic antibody responses that could not be quantified by image analysis software, two investigators performed a qualitative review of all images to identify and confirm positive responses, as described previously. Briefly, "ArrayPro Analyzer" (MediaCybernetics, Inc.) was used to adjust the original image to extremely high contrast and brightness, and each spot was graded from 0 to 5 according to the intensity and shape of the ring.
候选标志物选择Candidate Marker Selection
选择视觉分析显示肺腺癌中高频的蛋白质抗原接受进一步ELISA确认。具体地说，它们必须符合全部以下标准：1).它们的ADC中频率减去SMC中频率大于或等于2；2).ADC中频率除以SMC中频率大于或等于1.4。总共选得57个蛋白质抗原。Selected visual analysis showed protein antigens with high frequency in lung adenocarcinoma underwent further ELISA confirmation. Specifically, they must meet all of the following criteria: 1). Their frequency in ADC minus frequency in SMC is greater than or equal to 2; 2). Frequency in ADC divided by frequency in SMC is greater than or equal to 1.4. A total of 57 protein antigens were selected.
通路分析pathway analysis
用Cytoscape和ClueGo插件对全部57个蛋白质进行基因本体论项(term)富集分析，这些蛋白质带有我们的蛋白阵列所展示蛋白的定制注释。将基因符号用作标识符进行分析。节点大小与测得基因数量成正比。节点颜色编排为反映Benjamini-Hochberg校正p值。Gene Ontology term enrichment analysis was performed with Cytoscape and ClueGo plugins for all 57 proteins with custom annotations of proteins displayed by our protein array. Gene symbols were used as identifiers for analysis. Node size is proportional to the number of genes measured. Nodes are color-coded to reflect Benjamini-Hochberg corrected p-values.
ELISA试验ELISA test
如此前所述，用新生产的人蛋白质通过ELISA试验来确认选出的对蛋白质抗原的AAb应答。简言之，实验前一天，96孔高结合(“highbind”)ELISA板(康宁(Corning))用pH9.4的0.2M碳酸氢钠缓冲液配制的10μg/ml山羊抗GST抗体(GE医疗(GE Healthcare))包被，4℃过夜。全部高通量液体操作用BioMek NxP实验室自动工作站(贝克曼库尔特(BeckmanCoulter))进行。Selected AAb responses to protein antigens were confirmed by ELISA assays with freshly produced human proteins as described previously. Briefly, the day before the experiment, 96-well high-binding (“highbind”) ELISA plates (Corning) were treated with 10 μg/ml goat anti-GST antibody (GE Healthcare ( GE Healthcare)) at 4°C overnight. All high-throughput liquid manipulations were performed with a BioMek NxP laboratory automation workstation (Beckman Coulter).
蛋白质用HeLa细胞裂解物体外转录-翻译系统(赛默科技(Thermo Scientific))生产，然后在5％含乳PBST封闭的GST抗体-包被的ELISA板上进行捕获。然后将血浆样品用5％含乳PBST稀释至1:200，室温下振荡孵育1小时。偶联辣根过氧化物酶的抗人IgG用作二抗(杰克逊实验室(The Jackson Laboratory))。然后，孔板上加入TMB底物(赛默科技(Thermo Scientific))显色15分钟，添加2M硫酸终止反应。用Perkin Elmer读板器测定OD450。用所表达抗原的OD450相对于该样品所有测得抗原的中值OD450计算每份血浆样品-抗原反应(复合物)的ELISA相对吸光度。用中值对各份血浆样品的系统背景进行标准化。Proteins were produced using a HeLa cell lysate in vitro transcription-translation system (Thermo Scientific) and then captured on GST antibody-coated ELISA plates blocked with 5% milky PBST. Plasma samples were then diluted 1:200 with 5% milk-containing PBST and incubated for 1 hour at room temperature with shaking. Anti-human IgG conjugated to horseradish peroxidase was used as secondary antibody (The Jackson Laboratory). Then, TMB substrate (Thermo Scientific) was added to the well plate to develop color for 15 minutes, and 2M sulfuric acid was added to terminate the reaction. OD450 was determined with a Perkin Elmer plate reader. The ELISA relative absorbance of each plasma sample-antigen reaction (complex) was calculated using the OD450 of the expressed antigen relative to the median OD450 of all antigens measured for that sample. The system background of each plasma sample was normalized to the median value.
统计与数据分析Statistics and Data Analysis
为了将众AAb合并分组，我们以吸烟对照(组I)或良性对照(组II)的第98百分位相对吸光度作为截止值。如果对组内候选者之一的AAb应答超过其对应的截止值，则样本判为肺腺癌阳性。To pool all AAbs into groups, we used the 98th percentile relative absorbance of smoking controls (Group I) or benign controls (Group II) as the cutoff value. A sample was judged positive for lung adenocarcinoma if the AAb response to one of the candidates in the panel exceeded its corresponding cutoff value.
用确认ELISA结果制作热图来展示17个选定靶标在肺癌患者和吸烟者对照中的差异AAb应答。确定每个AAb的热图颜色标度(scale)，用R语言gplots程序包来构建。Confirmation ELISA results were used to generate a heat map to demonstrate differential AAb responses of 17 selected targets in lung cancer patients and smoker controls. Determine the heat map color scale (scale) for each AAb, and use the R language gplots package to build.
根据对每个抗原的ELISA分析，如果它们超过良性对象值的第98百分位，我们将对象归类为AAb响应者。我们构建了一个多变量逻辑回归模型，用来检测肺癌患者和良性对照中AAb应答与年龄、结节大小和吸烟史的关联性。第二个多变量逻辑回归添加肺癌病情作为独立变量，进一步评价结节大小、肺癌病情与AAb应答之间的关系。我们还构建了一个多变量逻辑回归模型用来分析肺癌患者中AAb应答与肿瘤大、结节情况和肿瘤分期之间的关联。We classified subjects as AAb responders if they exceeded the 98th percentile of values for benign subjects based on ELISA analysis for each antigen. We constructed a multivariate logistic regression model to examine the association of AAb response with age, nodule size, and smoking history in lung cancer patients and benign controls. A second multivariate logistic regression added lung cancer status as an independent variable to further evaluate the relationship between nodule size, lung cancer status, and AAb response. We also constructed a multivariate logistic regression model to analyze the association between AAb response and tumor size, nodularity, and tumor stage in lung cancer patients.
肺腺癌与正常组织之间TCGA mRNA表达水平的比较我们采用魏尔希(Welch’s)单边t试验。TCGA肺腺癌数据是用Illumina HiSeq生成的，来自加州大学圣克鲁兹癌症基因组浏览器(“UC Santa Cruz Cancer Genome Browser”(https://genome-cancer.ucsc.edu/)TCGA_LUNG_exp_HiSeqV2-2014-08-22。通过从每个样品中减去每个mRNA的平均值来对全部强度进行标准化。We used Welch's one-sided t test to compare the expression level of TCGA mRNA between lung adenocarcinoma and normal tissue. TCGA lung adenocarcinoma data were generated with Illumina HiSeq from the UC Santa Cruz Cancer Genome Browser (“UC Santa Cruz Cancer Genome Browser” (https://genome-cancer.ucsc.edu/ ) TCGA_LUNG_exp_HiSeqV2-2014-08- 22. Normalize the overall intensity by subtracting the mean value for each mRNA from each sample.
肺腺癌相关候选AAb的鉴定Identification of candidate AAbs associated with lung adenocarcinoma
为了鉴定肺腺癌相关候选AAb，我们首先在NAPPA上对40名肺腺癌患者和40名重度吸烟者对照的血浆样品中10,000种全长人蛋白质的抗体进行全面特征分析。基于该阵列数据，我们挑选了57个抗原，它们的AAb应答在肺癌患者中区别于吸烟者对照。对这57个候选AAb靶标的基因本体论富集分析发现它们参与胚胎形态发生、器官发育、激酶信号传导和中间纤维细胞骨架。然后，我们通过使用相同样品的ELISA对这些选出的候选抗原进行评价。根据ELISA，17个抗原被确认在肺癌患者中诱导差异AAb应答，这些进入下一步分析。To identify lung adenocarcinoma-associated candidate AAbs, we first comprehensively characterized antibodies to 10,000 full-length human proteins in plasma samples from 40 lung adenocarcinoma patients and 40 heavy smoker controls on NAPPA. Based on this array data, we selected 57 antigens whose AAb responses differentiated in lung cancer patients from smoker controls. Gene Ontology enrichment analysis of these 57 candidate AAb targets revealed their involvement in embryonic morphogenesis, organ development, kinase signaling, and intermediate fiber cytoskeleton. We then evaluated these selected candidate antigens by ELISA using the same samples. According to ELISA, 17 antigens were identified to induce differential AAb responses in lung cancer patients, and these entered the next step of analysis.
肺癌患者与健康吸烟者对照中的比较验证Comparative Validation in Lung Cancer Patients and Healthy Smoker Controls
为了确认肺癌患者中这17个AAb的水平，我们检测了来自97个病例和87个对照的另184份血浆样品中的这些AAb。此外，根据此前的文献，我们还将TP53和CTAG1B蛋白纳入作为可能的候选者。各个AAb的血清阳性截止值设定为这87个对照样本ELISA吸光度的第98百分位。肺癌患者与吸烟者对照相比较，TTC14、BRAF和CTAG1B的AAb的98％特异性灵敏度高于5％。此外，在全部样本集中，TTC14、BRAF、ACTL6B、MORC2和CTAG1B的AAb的98％特异性灵敏度高于5％。进一步分析这5个抗原，用各个抗原的标准化截止值即高于或等于吸烟者对照相对吸光度第98百分位的相对吸光度，结果发现一个5-AAb组(组I)，89％特异性灵敏度为30％。To confirm the levels of these 17 AAbs in lung cancer patients, we detected these AAbs in another 184 plasma samples from 97 cases and 87 controls. In addition, we also included TP53 and CTAG1B proteins as possible candidates based on previous literature. The seropositivity cutoff for each AAb was set at the 98th percentile of the ELISA absorbance of these 87 control samples. The 98% specific sensitivity of AAbs to TTC14, BRAF and CTAG1B was higher than 5% in lung cancer patients compared to smoker controls. In addition, the 98% specific sensitivity of AAbs for TTC14, BRAF, ACTL6B, MORC2, and CTAG1B was higher than 5% in all sample sets. Further analysis of these 5 antigens, using the normalized cut-off value of each antigen that is higher than or equal to the relative absorbance of the 98th percentile relative absorbance of the smoker control, it was found that a 5-AAb group (group I) with a specific sensitivity of 89% 30%.
肺癌与良性对照的比较分类Comparative Classification of Lung Cancers and Benign Controls
为了检验这17个AAb与TP53和CTAG1B的AAb一起是否能够区分肺癌和CT筛查查出的良性疾病，我们对267份血浆样品中这些抗原的AAb应答进行了ELISA分析。如前所述，各个AAb的截止值设定为良性对照相对吸光度的第98百分位值。KRT8、TTC14、KLF8、BRAF、TLK1被确认为相比良性对照与肺癌患者关联。它们的总体98％特异性灵敏度高于5％。To test whether these 17 AAbs, together with those of TP53 and CTAG1B, could distinguish lung cancer from benign disease detected by CT screening, we performed ELISA analysis of AAb responses to these antigens in 267 plasma samples. The cutoff value for each AAb was set at the 98th percentile of the relative absorbance of the benign control as previously described. KRT8, TTC14, KLF8, BRAF, TLK1 were identified as being associated with lung cancer patients compared to benign controls. Their overall 98% specific sensitivity was higher than 5%.
进一步分析这5个抗原，用各个抗原的标准化截止值即高于或等于良性对照相对吸光度第98百分位的相对吸光度，结果发现一个5-AAb组(组II)，88％特异性灵敏度为30％。然后我们评价了这些候选AAb区分肺癌与良性对照的能力，对照的吸烟史吸烟指数超过30，以此模拟目标人群。以上5个AAb之外，NF2和CTTNBP2NL的AAb也表现为98％特异性灵敏度高于5％。还评价了各个AAb采用不同良性肺结节对象时的灵敏度。These 5 antigens were further analyzed, and the normalized cut-off value of each antigen was higher than or equal to the relative absorbance of the 98th percentile relative absorbance of the benign control. As a result, it was found that a 5-AAb group (group II) had an 88% specific sensitivity of 30%. We then evaluated the ability of these candidate AAbs to distinguish lung cancer from benign controls with a smoking history of more than 30 to simulate the target population. In addition to the above 5 AAbs, the AAbs of NF2 and CTTNBP2NL also showed 98% specificity and sensitivity higher than 5%. The sensitivity of each AAb was also evaluated using different subjects with benign pulmonary nodules.
患者和疾病特征对AAb阳性率的影响Influence of patient and disease characteristics on AAb positive rate
我们比较了吸烟史、肿瘤大小、性别和年龄这些临床风险因子对AAb应答的影响。采用来自组II的明确截止值，AAb响应者与非响应者之间，就性别(P＝0.212)、年龄(P＝0.818)或吸烟史(P＝0.635)而言没有显著差异，但是结节大小被发现与AAb应答显著相关(P＝0.025)。由于良性对照与病例未在结节大小上进行匹配，就病情进行调整后，相关性不再具备显著性(P＝0.752)。We compared the effects of clinical risk factors such as smoking history, tumor size, sex, and age on AAb response. Using explicit cutoffs from group II, there were no significant differences between AAb responders and non-responders with respect to gender (P=0.212), age (P=0.818), or smoking history (P=0.635), but nodular Size was found to be significantly correlated with AAb response (P=0.025). Since benign controls and cases were not matched in nodule size, the correlation was no longer significant (P = 0.752) after adjustment for disease.
这一结果提示，这些AAb组扩充了肺癌病情信息，并且，观测到的AAb应答与已知风险因子无关。此外，我们还分析了肺癌患者中的AAb应答与患者特征包括吸烟史、肿瘤大小、结节情况和肿瘤分期之间的关联。没有发现这些患者特征与AAb应答之间有显著关联。尽管没有统计学显著性，我们发现TTC14的AAb在I期肺癌中较多，而BRAF的AAb则在II期和III期中较多。These results suggest that these AAb panels expand information on lung cancer conditions and that the observed AAb responses were independent of known risk factors. In addition, we analyzed the association between AAb response in lung cancer patients and patient characteristics including smoking history, tumor size, nodular status, and tumor stage. No significant association was found between these patient characteristics and AAb response. Although not statistically significant, we found that AAbs to TTC14 were more in stage I lung cancers, whereas AAbs to BRAF were more in stages II and III.
AAb靶标与其mRNA水平之间的关联Association between AAb targets and their mRNA levels
我们还用TCGA数据分析了两组蛋白质抗原的组织mRNA水平。8种蛋白质中6种表现为相比正常组织在肺腺癌组织中表达显著升高。这一正交分析确认了我们的发现即这些AAb关联肺腺癌。We also analyzed tissue mRNA levels of protein antigens in both groups using TCGA data. Six of the eight proteins showed significantly higher expression in lung adenocarcinoma tissues than in normal tissues. This orthogonal analysis confirms our finding that these AAbs are associated with lung adenocarcinoma.
采用免疫-蛋白组学方法，我们分析了健康、重度吸烟者对照和肺癌患者的抗体应答特征。生物信息分析发现显著富集的胚胎形态发生、器官发育(包括肺发育)和信号传导相关通路和丝氨酸/苏氨酸激酶通路。用包括良性结节对象的扩展样品集用ELISA确认了抗体子集的性能，98％特异性灵敏度为5-10％。Using an immuno-proteomics approach, we analyzed the antibody response profile of healthy, heavy smoker controls, and lung cancer patients. Bioinformatic analysis revealed significantly enriched embryonic morphogenesis, organ development (including lung development) and signal transduction-related pathways and serine/threonine kinase pathways. The performance of the antibody subset was confirmed by ELISA with an extended sample set including benign nodular subjects, with 98% specificity and sensitivity of 5-10%.
我们报道一个5-AAb组(TTC14、BRAF、ACTL6B、MORC2、CTAG1B)区分肺癌与有吸烟史高风险对照的89％特异性灵敏度为30％。肺癌与CT阳性肺结节患者的AAb应答比较发现一个相关但不同的5-AAb组(TTC14、BRAF、KLF8、TLK1、KRT8)，88％特异性灵敏度为30％。We report a 5-AAb panel (TTC14, BRAF, ACTL6B, MORC2, CTAG1B) with 89% specificity and 30% sensitivity for distinguishing lung cancer from high-risk controls with a history of smoking. A comparison of AAb responses in patients with lung cancer and CT-positive pulmonary nodules identified a related but distinct group of 5-AAbs (TTC14, BRAF, KLF8, TLK1, KRT8) with 88% specificity and 30% sensitivity.
我们还计算了进入验证试验的全部19个候选抗原的灵敏度和特异性，所述验证试验要求良性样本的吸烟史吸烟指数达30或30以上。报道的全部五个候选抗原的98％特异性灵敏度仍然都高于5％，表明他们仍然能够区分肺腺癌与良性对照(不显示)。尽管这些组需进一步验证，它们确实提供了这些具有信息意义的抗原的互补性(complementarities)信息。进一步分析显示，AAb与肿瘤大小、分析和吸烟史没有关联。We also calculated the sensitivities and specificities of all 19 candidate antigens entered into a validation assay requiring a smoking history index of 30 or more for benign samples. The 98% specificity sensitivities reported for all five candidate antigens were still above 5%, indicating that they were still able to distinguish lung adenocarcinoma from benign controls (not shown). Although these panels require further validation, they do provide information on the complementarities of these informative antigens. Further analysis revealed no association of AAbs with tumor size, analysis, and smoking history.
尚不清楚哪些因素决定这些体液免疫应答的发生。假设AAb应答与相应蛋白质靶标的组织过表达相关联，则蛋白质过表达的患者中仅少部分会产生可测水平的AAb应答。我们还在TCGA肺腺癌数据集中分析了这些AAb靶标的mRNA水平。两组8种蛋白质中有6种的mRNA表达在肺腺癌组织中显著升高。这一发现不仅提示肺腺癌中AAb的产生可能是蛋白质过表达的结果，而且正交证明了这些AAb与肺腺癌关联。It is unclear which factors determine the development of these humoral immune responses. Assuming that AAb responses correlate with tissue overexpression of the corresponding protein target, only a minority of patients with protein overexpression will develop a measurable level of AAb response. We also analyzed the mRNA levels of these AAb targets in the TCGA lung adenocarcinoma dataset. The mRNA expressions of 6 out of 8 proteins in both groups were significantly increased in lung adenocarcinoma tissues. This finding not only suggests that AAb production in lung adenocarcinoma may be the result of protein overexpression, but also demonstrates orthogonally that these AAbs are associated with lung adenocarcinoma.
本项研究的优势包括采用了来自吸烟者对照以及CT阳性良性肺病对照的配对早期(I期)NSCLC腺癌的大量血浆样品。我们还采用了具有高度再现性的蛋白阵列来无偏高通量筛选候选AAb，由此发现了高信息量的通路，这些通量涉及发育过程和激酶信号传导。Strengths of this study include the use of large plasma samples from matched early (stage I) NSCLC adenocarcinomas in smoker controls and CT-positive controls with benign lung disease. We also employed highly reproducible protein arrays for unbiased high-throughput screening of candidate AAbs, resulting in the discovery of highly informative pathways involved in developmental processes and kinase signaling.
为了评价这些AAb的性能，我们在大样本集中采用具有临床相关性的ELISA试验，并进行了独立盲测。我们的结果与TCGA mRNA表达数据也一致。本项研究的一个局限在于，研究开始时我们没有将患者随机分成发现集和验证集。这导致验证集中晚期样本居多。未来有必要用早期样本进行验证来确认我们的标志物的性能。尽管这是目前肺癌领域最大的自身抗体研究之一，并且是唯一采用患者和CT筛查良性对照的研究，我们的样本数量仍然偏小从而无法在多变量分析中得出确定的结论。我们的研究聚焦组织学上同质的腺癌患者。然而，我们承认，未来需要进一步在按照组织学亚型和分子亚型分级的腺癌亚型中研究和评价AAb应答和疾病的异质性。此外，本项研究依赖于对野生型蛋白进行的针对肺癌关联抗体的蛋白组筛选。随着编码具有已知癌症相关突变的蛋白质的cDNA克隆的获得，进一步包括这些将是有用的。To evaluate the performance of these AAbs, we employed a clinically relevant ELISA assay in a large sample set and performed independent blind tests. Our results are also consistent with the TCGA mRNA expression data. A limitation of this study is that we did not start the study by randomizing patients into discovery and validation sets. This results in a majority of late samples in the validation set. Future validation with earlier samples will be necessary to confirm the performance of our markers. Although this is one of the largest autoantibody studies in lung cancer to date, and the only study using patients and CT-screened benign controls, our sample size was too small to draw firm conclusions in a multivariate analysis. Our study focused on histologically homogeneous adenocarcinoma patients. However, we acknowledge that further studies and evaluations of AAb response and disease heterogeneity in adenocarcinoma subtypes graded by histologic and molecular subtypes are needed in the future. In addition, this study relied on a proteomic screen of the wild-type protein against lung cancer-associated antibodies. As cDNA clones encoding proteins with known cancer-associated mutations become available, it would be useful to further include these.
概括来说，我们用蛋白阵列进行了AAb应答的免疫-蛋白组学筛选，鉴定到两组AAb，潜在地，它们能够区分肺腺癌与吸烟者对照以及CT阳性良性肺病。BRAF，一个推定致癌基因，还被发现在肺癌患者中诱发体液免疫应答。就本项研究而言，我们聚焦高特异性标志物，因此，CT筛查阳性和血清检测阳性的高风险对象应当接受更高侵入性的检查例如穿刺活检，为了癌症的及时诊断。In summary, we performed an immuno-proteomic screen of AAb responses using protein arrays and identified two groups of AAbs that, potentially, could differentiate lung adenocarcinoma from smoker controls as well as CT-positive benign lung disease. BRAF, a putative oncogene, was also found to induce humoral immune responses in lung cancer patients. For the purpose of this study, we focus on high-specificity markers, therefore, high-risk subjects with positive CT screening and positive serum test should undergo more invasive examinations such as needle biopsy for timely diagnosis of cancer.
权利要求不应局限到本文中的具体实施方式和实施例。The claims should not be limited to the detailed description and examples herein.
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